Understanding willingness to participate in private shared parking under incentive and disincentive policies

While shared parking offers a promising solution to alleviate urban parking challenges, its adoption in private parking spaces, particularly in residential neighborhoods, is lagging due to low market participation. Successful implementation of private shared parking mainly hinges on understanding the factors that motivate individuals to participate in sharing programs. Existing research has preliminarily investigated parking space owners’ and users’ attitudes toward private shared parking, but has often overlooked the impact of policy interventions on willingness to participate (WTP), as well as the complex nonlinear effects and interaction relationships of influencing factors. To address these gaps, formal survey data on WTP in private shared parking programs are first collected from parking space owners and users under varying monetary-linked incentives and disincentives in China. Then, the nonlinear and interaction effects of influencing variables on WTP are uncovered using XGBoost and SHAP interpretable machine learning techniques. The results reveal that compared with the baseline methods, the adopted model demonstrates superior performance in predicting WTP in private shared parking; Personal attributes, along with rewards and penalties, emerge as the most influential factor categories and exhibit significant nonlinear and interaction effects; The effect of key factors, such as reward bonus, penalty payment, education, average monthly income, parking space idle duration, concerns about sharing revenues, weekly driving frequency, having used shared parking, varies substantially across unwilling, neutral, and willing attitudes of parking space owners and users. The findings could help provide valuable insights for government agencies and management operators to further improve private shared parking initiatives and expand their markets.